Multi-way modeling of hydro-chemical data of an alluvial river system--a case study

Heavy metal speciation, leaching and toxicity status of a tropical rain-fed river Damodar, India

Speciations of metals were assessed in a tropical rain-fed river, flowing through the highly economically important part of the India. The pattern of distribution of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn) were evaluated in water and sediment along with mineralogical characterization, changes with different water quality parameters and their respective health hazard to the local population along the Damodar River basin during pre-monsoon and post-monsoon seasons. The outcome of the speciation analysis using MINTEQ indicated that free metal ions, carbonate, chloride and sulfate ions were predominantly in anionic inorganic fractions, while in cationic inorganic fractions metal loads were negligible. Metals loads were higher in sediment phase than in the aqueous phase. The estimated values of I_geo in river sediment during both the seasons showed that most of the metals were found in the I_geo class 0-1 which represents unpolluted to moderately polluted sediment status. The result of partition coefficient indicated the strong retention capability of Cr, Pb, Co and Mn, while Cd, Zn, Cu and Ni have resilient mobility capacity. The mineralogical analysis of sediment samples indicated that in Damodar River, quartz, kaolinite and calcite minerals were dominantly present. The hazard index values of Cd, Co and Cr were > 1 in river water, which suggested potential health risk for the children. A combination of pragmatic, computational and statistical relationship between ionic species and fractions of metals represented a strong persuasion for identifying the alikeness among the different sites of the river.

Influence of ligands on metal speciation, transport and toxicity in a tropical river during wet (monsoon) period

Metal speciation and transport are seldom assessed in densely populated Tropical River. An evaluation of the phase distribution for Copper (Cu), Lead (Pb) and Zinc (Zn) along with chemical speciation, variance with different water quality parameters and toxicity were conducted in the Brahmaputra River of India from upstream to downstream during wet (monsoon) periods in July 2014. Results indicated that metal free ions and carbonates were dominant in the inorganic fractions whereas metal concentrations were negligible in the anionic inorganic fractions. Due to high sediment load in the river during monsoon, metals were substantially higher in the particulate fractions than in the aqueous phase. Partition coefficient for Cu (3.1-6.1), Pb (3.4-6.5) and Zn (3.5-6.9), demonstrated strong adsorption of the metals on suspended matter. Q-mode hierarchical cluster analysis (HCA) illustrated groupings mainly governed by quality parameters rather than by the river course. R-mode results imply selectivity of the affinities of metals for different ligands. Health risk index (HRI) values were less than 1 for dissolved metal for Cu, Pb and Zn while it was greater than 1 for total metal for Pb and Cu indicating potential human health risk. The study demonstrated that binding of metals with naturally occurring dissolved organic matter or suspended particulate matter affects metal bioavailability in river during wet periods when sediment load is particularly high. A combination of empirical, computational and statistical relationships between ionic species and fractions of metals provided greater certitude in identifying the resemblance among the different locations of the river.

Chemometrics quality assessment of wastewater treatment plant effluents using physicochemical parameters and UV absorption measurements

Chemometric techniques like Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) are used to explore, analyze and model relationships among different water quality parameters in wastewater treatment plants (WWTP). Different data sets generated by laboratory analysis and by an automatic multi-parametric monitoring system with a new designed optical device have been investigated for temporal variations on water quality parameters measured in the water influent and effluent of a WWTP over different time scales. The obtained results allowed the discovery of the more important relationships among the monitored parameters and of their cyclic dependence on time (daily, monthly and annual cycles) and on different plant management procedures. This study intended also the modeling and prediction of concentrations of several water components and parameters, especially relevant for water quality assessment, such as Dissolved Organic Matter (DOM), Total Organic Carbon (TOC) nitrate, detergent, and phenol concentrations. PLS models were built to correlate target concentrations of these constituents with UV spectra measured in samples collected at (1) laboratory conditions (in synthetic water mixtures); and at (2) WWTP conditions (in real water samples from the plant). Using synthetic water mixtures, specific wavelengths were selected with the aim to establish simple and reliable prediction models, which gave good relative predictions with errors of around 3-4% for nitrates, detergent and phenols concentrations and of around 15% for the DOM in external validation. In the case of nitrate and TOC concentrations modeling in real water samples from the effluent of the WWTP using the reduced spectral data set, results were also promising with low prediction errors (less than 20%).

Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Sukhnag Stream: An Inflow Stream of Lake Wular (Ramsar Site), Kashmir Himalaya

The precursors of deterioration of immaculate Kashmir Himalaya water bodies are apparent. This study statistically analyzes the deteriorating water quality of the Sukhnag stream, one of the major inflow stream of Lake Wular. Statistical techniques, such as principal component analysis (PCA), regression analysis, and cluster analysis, were applied to 26 water quality parameters. PCA identified a reduced number of mean 2 varifactors, indicating that 96% of temporal and spatial changes affect the water quality in this stream. First factor from factor analysis explained 66% of the total variance between velocity, total-P, NO3–N, Ca2+, Na+, TS, TSS, and TDS. Bray-Curtis cluster analysis showed a similarity of 96% between sites IV and V and 94% between sites II and III. The dendrogram of seasonal similarity showed a maximum similarity of 97% between spring and autumn and 82% between winter and summer clusters. For nitrate, nitrite, and chloride, the trend in accumulation factor (AF) showed that the downstream concentrations were about 2.0, 2.0, and 2.9, times respectively, greater than upstream concentrations.

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.

Assessment of temporal variation in water quality of some important rivers in middle Gangetic plains, India

The study explains water quality of three important tributaries of the Ganga River in the middle Gangetic plains in India. Seasonal changes in the water quality of the studied rivers: Gandak, Ghaghra, and Sone were observed. During monsoon, several water quality parameters show considerable changes due to increased runoff from the catchments and other seasonal factors. Multivariate discriminant analysis delineated a few parameters responsible for temporal variation in water quality. Seasonal variation in water quality of the Gandak River was rendered by seven parameters-turbidity, sulfate, pH, phosphate, water temperature, total alkalinity, and sodium, while total alkalinity and water temperature were responsible for seasonal discrimination in water quality of Ghaghra River. Water temperature, turbidity, total dissolved solids, total suspended solids, calcium, and phosphate were important for seasonal discrimination in water quality of Sone River. The seasonal changes in water quality of the rivers were due to seasonal effects and catchment characteristics. The discriminant functions classified most of the cases correctly.

Vertical characterization of soil contamination using multi-way modeling--a case study

This study describes application of chemometric multi-way modeling approach to analyze the dataset pertaining to soils of industrial area with a view to assess the soil/sub-soil contamination, accumulation pathways and mobility of contaminants in the soil profiles. The three-way (sampling depths, chemical variables, sampling sites) dataset on heavy metals in soil samples collected from three different sites in an industrial area, up to a depth of 60 m each was analyzed using three-way Tucker3 model validated for stability and goodness of fit. A two component Tucker3 model, explaining 66.6% of data variance, allowed interpretation of the data information in all the three modes. The interpretation of core elements revealing interactions among the components of different modes (depth, variables, sites) allowed inferring more realistic information about the contamination pattern of soils both along the horizontal and vertical coordinates, contamination pathways, and mobility of contaminants through soil profiles, as compared to the traditional data analysis techniques. It concluded that soils at site-1 and site-2 are relatively more contaminated with heavy metals of both the natural as well as anthropogenic origins, as compared to the soil of site-3. Moreover, the accumulation pathways of metals for upper shallow layers and deeper layers of soils in the area were differentiated. The information generated would be helpful in developing strategies for remediation of the contaminated soils for reducing the subsequent risk of ground-water contamination in the study region.

Chemometrics assisted spectrophotometric determination of pyridine in water and wastewater

The paper reports a direct method for the determination of pyridine in water and wastewater samples based on ultraviolet spectrophotometric measurements using multi-way modeling techniques. Parallel factor analysis (PARAFAC) and multi-way partial least squares (N-PLS) regression methods were employed for the decomposition of spectra and quantification of pyridine. The study was carried out in the pH range of 1.0-12.0 and concentration range of 0.67-51.7 microgmL(-1) of pyridine. Both the three-way PARAFAC and tri-PLS1 models successfully predicted the concentration of pyridine in synthetic (spiked) river water and field wastewater samples. The mean recovery obtained from PARAFAC regression model were 97.39% for the spiked and 99.84% for the field wastewater samples, respectively. The sensitivity and precision of the method for pyridine determination were 0.58% and 5.95%, respectively. The N-PLS regression model yielded mean recoveries of 99.29% and 100.18% for the spiked and field wastewater samples, respectively. The prediction accuracy of the methods was evaluated through the root mean square error of prediction (RMSEP). For PARAFAC, it was 0.65 and 0.82 microgmL(-1) for spiked river water and field wastewater samples, respectively, while for N-PLS, it was 0.25 and 0.37 microgmL(-1), respectively. Both the PARAFAC and N-PLS methods, thus, yielded satisfactory results for the prediction of pyridine concentration in water and wastewater samples.

Exploring groundwater hydrochemistry of alluvial aquifers using multi-way modeling

A three-way data set pertaining to hydrochemistry of the groundwater of north Indo-Gangetic alluvial plains was analyzed using three-way component analysis method with the purpose of extracting the information on spatial and temporal variation trends in groundwater composition. Three-way data modeling was performed using PARAFAC and Tucker3 models. The models were tested for their stability and goodness of optimal fit using core consistency diagnostic and split-half analysis. Although, a two-component PARAFAC model, explaining 50.47% of data variance, yielded 100% core consistency, it failed to qualify the validation test. Tucker3 model (3, 3, 1) captured 55.18% of the data variance and yielded simple diagonal core with three significant elements, explaining 100% of the core variability. Interpretation of the information obtained through Tucker3 model revealed that the groundwater quality in Khar watershed is mainly dominated by water hardness and related variables, whereas, water composition of the dug wells is dominated by alkalinity and carbonate/bicarbonates. Moreover, shallow groundwater sources in the region are contaminated with nitrate derived from fertilizers application in the region. The shallow aquifers are relatively more contaminated during the post-monsoon season.

Temporal characterisation of river waters in urban and semi-urban areas using physico-chemical parameters and chemometric methods

Three sampling campaigns were carried out in rivers located at two hydrographic basins affected by urban and semi-urban areas around the Metropolitan area of A Coruña (ca. 500,000 inhabitants, NW-Spain) to study local and temporal variations of 21 physicochemical parameters (pH, conductivity, Cl-, SO4(2-), SiO2, Ca2+, Mg2+, Na+, K+, hardness, NO3(-), NO2(-), NH4(+), COD, PO4(3-), Zn2+, Cu2+, Mn2+, Pb2+, alkalinity and acidity) in 23 sampling points. The temporal evolution of the water quality was assessed by matrix augmentation principal components analysis (MA-PCA) and parallel factor analysis (PARAFAC). Moreover, classical principal components analysis (PCA) (one per sampling campaign) was applied with exploratory and comparison purposes. The first factor of the different studies comprised variables associated to the mineral content and it differentiated the samples according to their hydrographic basins. The second factor was mainly associated to organic matter, from domestic wastes and decomposition of natural debris. The temporal evolution of the water quality was mostly related to seasonal increments of the physicochemical parameters defining the decomposition of the organic matter. The three models applied (PCA, MA-PCA and PARAFAC) led to similar conclusions, nonetheless, MA-PCA excelled, since the refolding of scores provided more straightforward and convenient overview of sample time and geographical variations than individual PCA and it is more flexible and adaptable to environmental studies than PARAFAC.